Speakers
Keynote Speakers
Robert Engle, Co-Director, The Volatility and Risk Institute and Professor Emeritus of Finance, NYU Stern School of Business
Robert Engle, Professor Emeritus of Finance at New York University Stern School of Business, was awarded the 2003 Nobel Prize in Economics for his research on the concept of autoregressive conditional heteroskedasticity (ARCH). He developed this method for statistical modeling of time-varying volatility and demonstrated that these techniques accurately capture the properties of many time series. Professor Engle shared the prize with Clive W. J. Granger of UCSD. Professor Engle is the Co-Director of the Volatility and Risk Institute at NYU Stern. In this role he has developed research tools to track risks in the global economy and make these publicly available on the V-LAB website. He is now actively investigating the risks from climate change and strategies for mitigation.
Andrew Patton is a Professor of Finance at UNSW Sydney and the Zelter Family Distinguished Professor of Economics and Professor of Finance at Duke University. His research interests lie in financial econometrics, with an emphasis on forecasting volatility and dependence, forecast evaluation methods, and empirical asset pricing. His research has appeared in a variety of academic journals, including Econometrica, Journal of the American Statistical Association, Journal of Econometrics, and Journal of Finance, Review of Financial Studies and Journal of Financial Economics. He is currently serving as the President of the Society for Financial Econometrics, and he has served on the editorial boards of many leading journals in econometrics and finance, as well as on the Federal Reserve Board’s Model Validation Council. Patton has previously taught at the London School of Economics, the University of Oxford, and New York University. He completed his undergraduate studies in finance and statistics at the University of Technology, Sydney, and his PhD in economics at the University of California, San Diego.
Michael Wolf is a Professor of Econometrics and Applied Statistics at the University of Zurich, and holds a Ph.D. in Statistics from Stanford University. Before joining the University of Zurich's Department of Economics, he held positions at the University of California, Los Angeles (UCLA), Universidad Carlos III de Madrid, and Universitat Pompeu Fabra in Barcelona.
His research interests include resampling-based inference, multiple testing methods, the estimation of large-dimensional covariance matrices, and financial econometrics. His work has been published in leading journals such as The Annals of Statistics, Biometrika, Econometrica, Journal of the American Statistical Association, and The Review of Financial Studies.